Artificial intelligence (AI) has emerged as a transformative tool in plant protection, enabling improved accuracy, timeliness, and sustainability in pest and disease management. This review synthesizes the evolution of AI-driven technologies in plant protection, from early rule-based expert systems to modern machine learning, deep learning, computer vision, Internet of Things (IoT), robotics, and decision support systems. Numerous studies have demonstrated the potential of AI for pest detection, disease diagnosis, outbreak forecasting, and precision interventions using image analysis, sensor networks, and predictive models. Image-based pest and disease detection models commonly report accuracies ranging from 85-98% under controlled or semi-field conditions. At the same time, AI-guided precision spraying systems have achieved substantial reductions in pesticide use compared to conventional blanket applications. Despite rapid progress, challenges persist in areas such as data quality, regional generalization, infrastructure limitations, affordability, and integration with integrated pest management (IPM) frameworks. This review critically evaluates existing methodologies, summarizes key applications, and identifies research gaps and future directions for the responsible deployment of AI in sustainable plant protection. From a plant protection perspective, the value of AI lies not in replacing expert judgment, but in strengthening timely, evidence-based decisions within established integrated pest management frameworks under field conditions.